def test_projector_rois():
td = get_testdir()
ins = op.join(td, 'in.surf.gii')
outs = op.join(td, 'out.surf.gii')
hemi = toblerone.Hemisphere(ins, outs, 'L')
spc = toblerone.ImageSpace(op.join(td, 'ref.nii.gz'))
fracs = nibabel.load(op.join(td, 'sph_fractions.nii.gz')).get_fdata()
hemi.pvs = fracs.reshape(-1, 3)
puta = Surface.manual(0.25 * hemi.inSurf.points, hemi.inSurf.tris,
'L_Puta')
rois = {'L_Puta': puta}
proj = toblerone.projection.Projector(hemi, spc, rois=rois, cores=8)
ndata = np.ones(proj.n_nodes)
ndata[-1] = 2
vdata = proj.node2vol(ndata, True)
spc.save_image(vdata, 'n2v.nii.gz')
ndata = proj.vol2node(vdata, True)
print(ndata)
def load(cls, path):
"""
Load Projector from path in HDF5 format. This is useful for
performing repeated analyses with the same voxel grid and
cortical surfaces.
"""
f = h5py.File(path, 'r')
p = cls.__new__(cls)
# Recreate the reference ImageSpace first
p.spc = ImageSpace.manual(f['ref_spc_vox2world'][()],
f['ref_spc_size'][()])
if 'ref_spc_fname' in f: p.spc.fname = f['ref_spc_fname'][()]
n_vox = p.spc.size.prod()
# Now read out hemisphere specific properties
p._hemi_pvs = []
p.vox_tri_mats = []
p.vtx_tri_mats = []
p.hemi_dict = {}
p._roi_pvs = {}
for s in SIDES:
hemi_key = f"{s}_hemi"
if hemi_key in f:
# Read out the surfaces, create the Hemisphere
ins, outs = [ Surface.manual(
f[hemi_key][f'{s}{n}S_points'][()],
f[hemi_key][f'{s}{n}S_tris'][()], f'{s}{n}S')
for n in ['W', 'P'] ]
p.hemi_dict[s] = Hemisphere(ins, outs, s)
# Read out the PVs array for the hemi
p._hemi_pvs.append(f[hemi_key][f"{s}_pvs"][()])
# Recreate the sparse voxtri and vtxtri matrices.
# They are stored as a 3 x N array, where top row
# is row indices, second is column, then data
voxtri = f[hemi_key][f"{s}_vox_tri"][()]
assert voxtri.shape[0] == 3, 'expected 3 rows'
voxtri = sparse.coo_matrix(
(voxtri[2,:], (voxtri[0,:], voxtri[1,:])),
shape=(n_vox, ins.tris.shape[0]))
p.vox_tri_mats.append(voxtri.tocsr())
# Same convention as above
vtxtri = f[hemi_key][f"{s}_vtx_tri"][()]
assert vtxtri.shape[0] == 3, 'expected 3 rows'
vtxtri = sparse.coo_matrix(
(vtxtri[2,:], (vtxtri[0,:], vtxtri[1,:])),
shape=(ins.n_points, ins.tris.shape[0]))
p.vtx_tri_mats.append(vtxtri.tocsr())
if "subcortical_pvs" in f:
g = f["subcortical_pvs"]
for k in sorted(g.keys()):
p._roi_pvs[k] = g[k][()]
return p